3D Structure Databases - Uses for Biological Problem solving - EBI

3D Structure Databases - Uses for Biological
Problem solving
The course will teach the basic principles aspects of 3D database technology and the
associated tools for data analysis to bioscientists wishing to understand the wealth of
structure information available. The course is aimed at PhD students and postdocs to
give them a familiarity with how structure data can be used in their own projects.
Databases for 3D structural data for proteins and nucleic acids, together with the
associated access tools have matured into a major tool for molecular biology. The
course is intended to cover the background to relational databases and the
computational aspects of characterizing structure of biological macromolecules
The importance of databases in biological research has been stressed in the recent
Nature technology feature by Buckingham [1]. In the United States, the National
Science Foundation (NSF) has announced a new initiative, Biological Databases and
Informatics Program Announcement’ [2], with the belief that future advances in the
biological sciences will depend both upon the creation of new knowledge and upon
effective management of proliferating information. Further general background can
be found in references 3 and 4.
1. S. Buckingham Data’s future shock (2004) Nature 428, 774-777
2. Biological Databases and Informatics Program Announcement NSF 02-058
http://www.nsf.gov/pubs/2002/nsf02058/nsf02058.html
3. Michael Y. Galperin (2004) The Molecular Biology Database Collection: 2004
update. Nucleic Acids Research. 32, Database issue D3-D22
4. Andrej Sali, Robert Glaeser, Thomas Earnest & Wolfgang Baumeister (2003)
From words to literature in structural proteomics NATURE, 422, 216-225
Provisional Timetable
Lecturers:
Professor Janet Thornton Dr Sue Jones
Dr Roman Laskowski Dr Hannes Ponstingl
Dr Kim Henrick Dr Eugene Krissinel
Dr Phil McNeil Dr Thomas Oldfield
Dr Sameer Velankar Mr Adel Golovin
Mr Dimitris Dimitropoulos Dr Gerard Kleywegt
Dr Jaime Prilusky Dr Helen Berman
Dr Loredana Lo Conte Dr Christine Orengo
Professor Bob Spence Dr James Milner-White
Dr Tom Oldfield Dr John Westbrook
Dr. Philip E. Bourne Dr Robert Finn
Ms. Kyle Burkhardt
Monday 20 th September
9:00-9-40 Structure analysis Professor Janet Thornton (EBI)
1. Todd A.E, Orengo C.A, Thornton J.M. (2002) Plasticity of enzyme active sites. Trends
Biochem Sci. 27 419-26.

2. Steward RE, MacArthur MW, Laskowski RA, Thornton JM. (2003) Molecular basis of
inherited diseases: a structural perspective. Trends Genet. 19, 505-13.
3. Sanishvili R, Yakunin AF, Laskowski RA, Skarina T, Evdokimova E, Doherty-Kirby A,
Lajoie GA, Thornton JM, Arrowsmith CH, Savchenko A, Joachimiak A, Edwards AM.
(2003) Integrating structure, bioinformatics, and enzymology to discover function:
BioH, a new carboxylesterase from Escherichia coli. J Biol Chem. 278, 26039-45.
9:40-10:20 An overview of the RCSB Protein Data Bank Dr. Helen M. Berman
RCSB Protein Data Bank Rutgers, The State University of New Jersey
A description of the resources for data deposition, validation and query offered by the
RCSB PDB will be given.
1. Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T. N., Weissig, H.,
Shindyalov, I. N. and Bourne, P. E. (2000) The Protein Data Bank. Nucleic Acids
Res., 28, 235-242.
2. Berman, H.M., Battistuz, T., Bhat, T.N., Bluhm, W.F., Bourne, P.E., Burkhardt, K.,
Feng, Z., Gilliland, G.L., Iype, L., Jain, S., Fagan, P., Marvin, J., Padilla, D.,
Ravichandran, V., Schneider, B., Thanki, N., Weissig, H., Westbrook, J.D. and
Zardecki, C. (2002) The Protein Data Bank. Acta Crystallogr D 58, 899-907.
3. John Westbrook, Zukang Feng, Li Chen, Huanwang Yang and Helen M. Berman
(2003) The Protein Data Bank and structural genomics. Nucleic Acids Research, 31,
489–491
4. Bhat,T.N., Bourne,P., Feng,Z., Gilliland,G., Jain,S., Ravichandran,V., Schneider,B.,
Schneider,K., Thanki,N., Weissig,H. et al. (2001) The PDB data uniformity project.
Nucleic Acids Res., 29, 214-218.
5. Westbrook,J., Feng,Z., Jain,S., Bhat,T.N., Thanki,N., Ravichandran,V., Gilliland,G.L.,
Bluhm,W., Weissig,H., Greer,D.S. et al. (2002) The Protein Data Bank: unifying the
archive. Nucleic Acids Res., 30, 245-248.
10:20-11:00 Crystals, Symmetry and Protein Assemblies Dr Kim Henrick (EBI)
1. Henrick,K. and Thornton,J.M. (1998) PQS: a protein quaternary structure file
server. Trends Biochem. Sci., 23, 358-361.
11:00-11:30 coffee break
11:30-12:10 Protein-DNA Interactions: analysis and prediction Dr Sue Jones
The 3D structures of over 700 proteins bound to DNA molecules have been determined.
These proteins have diverse structural folds, and achieve binding and recognition of DNA in
many different ways. This lecture will give an overview of the prominent characteristics of
DNA-binding proteins, and explain how common physicochemical properties and conserved
structural motifs can be used in a predictive manner to identify novel DNA-binding proteins.
1. Jones S. & Thornton J.M. (2004) Searching for functional sites in protein
structures. Current Opinion in Chemical Biology. 8, p3-7.
2. Jones S, Shanahan H, Berman H.M. & Thornton J.M. (2003) Using
electrostatic potentials to predict DNA-binding sites on DNA-binding proteins.
Nucleic Acids Research 31, p7189-7198.
3. Jones S, Barker J, Nobeli I & Thornton JM. (2003): Using structural motifs to
identify proteins with DNA binding function. Nucleic Acids Research. 31,
p2811-2823.

4. Jones S. & Thornton J.M. (2003) Protein-DNA interactions: the story so far
and a new method for prediction. Comparative and Functional Genomics. 4,
p428-431.
5. Jones S, van Heyningen P, Berman HM & Thornton JM. (1999) Protein-DNA
interactions: a structural analysis. Journal of Molecular Biology 287, p877-
896.
Additional Notes on Protein-Protein Interactions: classification, analysis and prediction
Interactions between proteins are fundamental to many diverse biological processes including
signal transduction, enzyme inhibition and cell adhesion. These interactions can be classified
as ‘obligate’ or ‘non-obligate’. Obligate interactions form the basis of the quaternary structure
of multimeric proteins, and non-obligate interactions occur between proteins that exist
independently as well as in complexes. This lecture will give an overview of the
characteristics of protein-protein complexes from 3D structures, and explain how these
features vary dependant upon the class of the complex. A method for the prediction of protein
interaction sites will then be described based on the analysis of patches on the protein
surface.
�� Jones S & Thornton JM. (1999) Protein domain interfaces: characterisation and
comparison with oligomeric protein interfaces. Protein Engineering 13, p77-82.
�� Jones S & Thornton JM. (1997): Prediction of protein-protein interaction sites using
patch analysis. Journal of Molecular Biology 272, p133-143.
�� Jones S & Thornton JM. (1997): Analysis of protein-protein interaction sites using
surface patches. Journal of Molecular Biology 272, p121-132.
�� Jones S & Thornton JM. (1996): Principles of protein-protein interactions derived from
structural studies. Proceedings of the National Academy of Science (USA) 93, p13-20.
12:10-12:50 Using the ‘Thornton-Group’ WWW Database Services for Structural
Research Dr Roman Laskowski EBI
12:50-14:00 lunch
1. R.A. Laskowski, J.D. Watson and J.M. Thornton (2003). From Protein
structure to biochemical function. J. Struct. Funct Genomics, 4, 167-177.
2. Laskowski RA. (2001) PDBsum: summaries and analyses of PDB structures.
Nucleic Acids Res. 29 221-2.
3. Luscombe N M, Laskowski R A, Westhead D R, Milburn D, Jones S,
Karmirantzou M, Thornton J M (1998). New tools and resources for analysing
protein structures and their interactions. Acta Cryst., D54, 1132-1138.
4. Craig T. Porter, Gail J. Bartlett, and Janet M. Thornton (2004) The Catalytic
Site Atlas: a resource of catalytic sites and residues identified in enzymes
using structural data. Nucl. Acids. Res. 32: D129-D133.
5. G J Bartlett, C T Porter, N Borkakoti & J M Thornton, Journal of Molecular
Biology (2002) 324, 105-121.
14:00-14:40 Quaternary Structure Inference of Proteins from their Crystals Dr
Hannes Ponstingl EBI
Protein-Protein Interactions: The basic principles which determine the strength and
geometry of protein-protein complexes by Patch Analysis and other methods.

1. H. Ponstingl, T. Kabir & J. M. Thornton (2003) Automatic inference of protein
quaternary structure from crystals, J. Appl. Cryst. 36, 1116-1122.
2. H. Ponstingl, K. Henrick & J. M. Thornton (2000) Discriminating between
homodimeric and monomeric proteins in the crystalline state. Proteins 41, 47-57.
14:40-15:20 Validation of protein structures ... Or: just because it was
published in Nature, doesn't mean it's true ! Dr. Gerard Kleywegt Uppsala
Sweden
With the explosive growth of the number of experimentally determined
macromolecular structures, "structural awareness" is becoming an important aspect
of many disciplines, ranging from medicinal chemistry to cell biology. This means that
many scientists who have not been specifically trained in the area want to make use
of structural information in order to explain the molecular basis of their own research
findings, to plan new experiments, to design novel ligands, substrates or inhibitors,
etc. What these users of structural information often are unaware of is that there are
limitations to and uncertainties in the experimentally determined structures. A number
of protein structures have been published (often in prestigious journals) that turned
out to be partly or entirely incorrect. A few examples will be given, and simple ways in
which non-experts can assess the overall reliability of structures will be discussed.
However, as technology improves, such gross errors are less and less likely to occur.
On the other hand, mistakes in the details of the structures are much easier to make,
and concomitantly more difficult to detect. It is often in these details, however, that the
value of a structure lies, since they reveal the molecular basis of interactions. This is
particularly true for non-macromolecular entities (ligands, inhibitors, substrateanalogues,
sugars, ions, etc.). Some of the pitfalls and limitations of the use of
structural information will be discussed, with a view to structure-based design. In the
practical, some of the basics of protein structure validation will be reviewed, and the
use of various databases (PDB, PDBsum, PDBREPORT and EDS) to assess the
quality of deposited protein structures will be explained.
References:
1. G J Kleywegt, "Validation of protein crystal structures" (Topical review), Acta
Crystallographica, D56, 249-265 (2000).
2. AM Davis, SJ Teague & GJ Kleywegt, "Applications and limitations of X-ray
crystallographic data in structure-based ligand and drug design", Angewandte
Chemie International Edition, 42, 2718-2736 (2003)
3. EDS Viewer for structures and electron density maps – interpretation of validation
criteria
15:20-16:00 3D databases and data warehouse technology Dr Phil McNeil (EBI)
16:00-16:30 coffee break
16:30-17:10 Clustering of 3D structures and representative sets Dr Thomas
Oldfield EBI
1. Li,W., Jaroszewski,L. and Godzik,A. (2001) Clustering of highly homologous
sequences to reduce the size of large protein databases.Bioinformatics, 17,
282-283.
17:10-17:50 Sequences and 3D structures (Integration of 3D data and sequence
databases) Dr Sameer Velankar EBI
The "Structure integration with function, taxonomy and sequence (SIFTS) initiative"
aims to work towards the integration of various bioinformatics resources. One of the

major obstacles to the improved integration of structural databases such as MSD
and sequence databases like UniProt
, which are primary archival databases for
structure and sequence data, is the absence of up to date and well-maintained
mapping between corresponding entries. We have worked closely with the UniProt
group at the EBI to clean up the taxonomy and sequence cross-reference information
in the MSD and UniProt databases. The project was started in the year 2001 and has
resulted in creating a robust mechanisms for exchanging data between the two
primary data resources. This has dramatically improved the quality of annotation in
both databases and is aiding the continuing improvements of legacy data. In the
longer term this project will allow for not only the better and closer integration of
derived-data resources but will continue to improve the quality of all data in the
primary resources. This information is vital for the reliable integration of the sequence
family databases such as Pfam and
Interpro with the structure-oriented databases of
SCOP and CATH
. This information has been made available
to the eFamily group and now forms the basis of the
regular interchange of information between the member databases (MSD, Uniprot,
Pfam, Interpro, SCOP and CATH).

Tuesday 21 st September
Participants split into 2 groups.
Morning session Group 1
Tutorials (15 min intro/demo – 45 minute tutorial)
�� Searching 3D protein structures for conserved DNA-binding motifs: Dr
Sue Jones
�� Using the ‘Thornton-Group’ WWW Database Services for Structural
Research Dr Roman Laskowski EBI (2hours)
CATRES, Catalytic Site Atlas (CSA), NetFunc, EC->PDB, Pita - Protein
InTerfaces and Assemblies, Receptor Structure and Function, Protein Side-
Chain Interactions, Practical: Structural Genomics
�� Pfam and MEROPS Robert Finn Sanger
Morning session Group 2
Lectures (40 minutes – intro/demos)
�� Protein Structure Classification and Genome Annotation: Technologies
and Insights from the CATH Database. Professor Christine Orengo UCL
London UK
�� SCOP: Structural Classification of Proteins. Dr Loredana Lo Conte (LMB
Cambridge UK)
�� SSM fold characterization Dr Eugene Krissinel EBI
Afternoon session Group 1
Lectures (40 minutes - intro/demos)
�� Protein Structure Classification and Genome Annotation: Technologies
and Insights from the CATH Database. Professor Christine Orengo UCL
London UK
�� SCOP: Structural Classification of Proteins. Dr Loredana Lo Conte (LMB
Cambridge UK)
�� SSM fold characterization Dr Eugene Krissinel EBI
Afternoon session Group 2
Tutorials (15 min intro/demo – 45 minute tutorial)
�� Searching 3D protein structures for conserved DNA-binding motifs: Dr
Sue Jones
�� Using the ‘Thornton-Group’ WWW Database Services for Structural
Research Dr Roman Laskowski EBI (2hours)
CATRES, Catalytic Site Atlas (CSA), NetFunc, EC->PDB, Pita - Protein
InTerfaces and Assemblies, Receptor Structure and Function, Protein Side-
Chain Interactions, Practical: Structural Genomics
�� Pfam and MEROPS Robert Finn Sanger
[ Motif and protein structures Dr. Gerard Kleywegt Uppsala Sweden ]
Protein Structure Classification and Genome Annotation: Technologies and
Insights from the CATH Database. Professor Christine Orengo UCL London UK
CATH is a novel hierarchical classification of protein domain structures, which
clusters proteins at four major levels, Class(C), Architecture(A), Topology(T) and
Homologous superfamily (H). Class, derived from secondary structure content, is

assigned for more than 90% of protein structures automatically. Architecture, which
describes the gross orientation of secondary structures, independent of
connectivities, is currently assigned manually. The topology level clusters structures
according to their toplogical connections and numbers of secondary structures. The
homologous superfamilies cluster proteins with highly similar structures and
functions. The assignments of structures to toplogy families and homologous
superfamilies are made by sequence and structure comparisons.
I will illustrate concepts behind protein structure comparison and classifiction using
the CATH database. I will also present methods for providing structural annotations
for genome sequences.
1. Orengo, C. A., Michie, A. D., Jones, S., Jones, D. T., Swindells, M. B. and
Thornton, J. M. (1997) CATH–a hierarchic classification of protein domain
structures. Structure, 5, 1093-1108.
2. Pearl, F.M.G, Lee, D., Bray, J.E, Sillitoe, I., Todd, A.E., Harrison, A.P.,
Thornton, J.M. and Orengo, C.A. (2000) Assigning genomic sequences to
CATH Nucleic Acids Research. 28. 277-282
SCOP: Structural Classification of Proteins. Dr Loredana Lo Conte (LMB
Cambridge UK)
Nearly all proteins have structural similarities with other proteins and, in some of
these cases, share a common evolutionary origin. The SCOP database, created by
manual inspection and abetted by a battery of automated methods, aims to provide a
detailed and comprehensive description of the structural and evolutionary
relationships between all proteins whose structure is known. As such, it provides a
broad survey of all known protein folds, detailed information about the close relatives
of any particular protein, and a framework for future research and classification.
I will take you behind the scenes. All we learned so far. What is missing.
1. Murzin, A. G., Brenner, S. E., Hubbard, T. and Chothia, C. (1995) SCOP: a
structural classification of proteins database for the investigation of
sequences and structures. J. Mol. Biol., 247, 536-540.
2. Andreeva A., Howorth D., Brenner S.E., Hubbard T.J.P., Chothia C., Murzin
A.G. (2004). SCOP database in 2004: refinements integrate structure and
sequence family data. Nucl. Acid Res. 32, D226-D229.
3. Lo Conte L., Brenner S. E., Hubbard T.J.P., Chothia C., Murzin A. (2002).
SCOP database in 2002: refinements accommodate structural genomics.
Nucl. Acid Res. 30, 264-267.
Pfam and MEROPS Robert Finn Sanger
Pfam is a database of two parts, the first is the curated part of Pfam containing over
7459 protein families. To give Pfam a more comprehensive coverage of known
proteins we automatically generate a supplement called Pfam-B. This contains a
large number of small families taken from the PRODOM database that do not overlap
with Pfam-A. Although of lower quality Pfam-B families can be useful when no Pfam-
A families are found.
1. The Pfam Protein Families Database Alex Bateman, Lachlan Coin, Richard Durbin,
Robert D. Finn, Volker Hollich, Sam Griffiths-Jones, Ajay Khanna, Mhairi Marshall,
Simon Moxon, Erik L. L. Sonnhammer, David J. Stud holme, Corin Yeats and Sean
R. Eddy (2004) Nucleic Acids Research Database Issue 32, D138-D141

2. Falquet L, Pagni M, Bucher P, Hulo N, Sigrist CJ, Hofmann K, Bairoch A. (2002). The
PROSITE database, its status in 2002 Nucleic Acids Res. 30:235-238
3. Julie D. Thompson, Fédéric Plewniak, Raymond Ripp, Jean-Claude Thierry and
Olivier Poch (2001) Towards a Reliable Objective Function for Multiple Sequence
Alignments. J.Mol.Biol. 314, 937-951
4. Servant F, Bru C, Carrère S, Courcelle E, Gouzy J, Peyruc D, Kahn D (2002)
ProDom: Automated clustering of homologous domains. Briefings in Bioinformatics.
3, 246-251
SSM fold characterization Dr Eugene Krissinel EBI
SSM is a powerful interactive research tool for secondary structure matching that
allows for comparing protein structures in 3D. The service provides for (i) pairwise
comparison and 3D alignment of protein structures, (ii) multiple comparison and 3D
alignment of protein structures, (iii) examination of a protein structure for similarity
with the whole PDB or SCOP archives, (iv) best Ca-alignment of compared structures
and (v) the ability to download and visualization of best-superposed structures using
RasMol or RasTop. The results are linked to other services including OCA, SCOP,
GeneCensus, FSSP, 3Dee, CATH, PDBsum, SwiisProt and ProtoMap. SSM is
recognized as a valuable tool in protein research, an aid to study protein function via
structural similarities (used in drug design), protein conformations, choice of model for
structure solution in X-ray experiments and many others. The development has also
extensive integration with other MSD services.
1. E. Krissinel and K. Henrick, Protein structure comparison in 3D based on secondary
structure matching (SSM) followed by Ca alignment, scored by a new structural
similarity function. In: Andreas J. Kungl & Penelope J. Kungl (Eds.), Proceedings of
the 5th International Conference on Molecular Structural Biology, Vienna, September
3-7, 2003, p.88.
2. E. Krissinel and K. Henrick, Common subgraph isomorphism detection by bactracking
search. (2004) Software: Practice and Experience, 34, 591-607.
Motif and protein structures Dr. Gerard Kleywegt Uppsala Sweden
Motif recognition (essentially, function-from-structure)
SPASM server Motif recognition in nucleic acids structures (SPANA)
Motif recognition in proteins (SPASM)
1. Madsen, D. and Kleywegt, G.J. (2002). Interactive motif and fold recognition in
protein structures. J. Appl. Cryst. 35, 137-139.

WEDS 22 nd September
Participants split into 2 groups.
Morning session Group 1
Tutorials (15 min intro/demo –45 minute tutorial)
�� Validation of protein structures ... Or: just because it was published in
Nature, doesn't mean it's true ! Dr. Gerard Kleywegt Uppsala Sweden
(90min)
�� Learning about structures using the RCSB PDB Dr. Philip E. Bourne
Professor of Pharmacology University of California (60min)
�� Evaluation of structure quality tutorial, using RCSB tools Ms. Kyle
Burkhardt RCSB Protein Data Bank Rutgers, The State University of New
Jersey (60min)
Morning session Group 2
Lectures (7x30mins - intro/demo MSD tools)
�� MSDchem, MSDlite, MSDpro, MSDsite
�� MSDmySQL , MSDmine
�� Advanced AstexViewer integrated into 3D PDB searches
Generic search systems for the search database has been written and made a public
service as http://www.ebi.ac.uk/msd-srv/msdlite and http://www.ebi.ac.uk/msdsrv/msdpro.
The system is written using java servlets and uses XML extensively for
configuration of the database/search system interactions, the description of the user
interface, and the return of results. The system is designed to translate user input
from a series of values into an SQL query, which can then be executed on the
database. The architecture of the server-side of the search system allows a high
degree of flexibility and extending the range of searches SQL statements that can be
created automatically). The system is highly configurable and can be moved easily to
other databases simply by modifying XML dictionaries, which describe the database.
Afternoon session Group 1
Lectures (7x30mins - intro/demo MSD tools)
�� MSDchem, MSDlite, MSDpro, MSDsite
�� MSDmySQL , MSDmine
�� Advanced AstexViewer integrated into 3D PDB searches
Afternoon session Group 2
Tutorials
�� Validation of protein structures ... Or: just because it was published in
Nature, doesn't mean it's true ! Dr. Gerard Kleywegt Uppsala Sweden
(90min)
�� Learning about structures using the RCSB PDB Dr. Philip E. Bourne
Professor of Pharmacology University of California (60min)
�� Evaluation of structure quality tutorial, using RCSB tools Ms. Kyle
Burkhardt RCSB Protein Data Bank Rutgers, The State University of New
Jersey (60min)
Learning about structures using the RCSB PDB Dr. Philip E. Bourne Professor of
Pharmacology University of California,

The RCSB PDB has been reengineered to include many new features and to
integrate a variety of additional information related to macromolecular structure and
function ranging from genomic information to disease states. A number of these
features will be explored through several typical usage scenarios suited to novice
users and more senior biologists.
1. Philip E. Bourne, Kenneth J. Addess, Wolfgang F. Bluhm, Li Chen,
Nita Deshpande, Zukang Feng, Ward Fleri, Rachel Green, Jeffrey C.
Merino-Ott, Wayne Townsend-Merino, Helge Weissig, John
Westbrook and Helen M. Berman (2004). The distribution and query
systems of the RCSB Protein Data Bank Nucleic Acids Research 32,
Database issue D223-D225
Evaluation of structure quality tutorial, using RCSB tools Ms. Kyle Burkhardt
RCSB Protein Data Bank Rutgers, The State University of New Jersey
In this one hour tutorial, users will learn how to evaluate the quality of a structure.
Users will download a structure from the PDB and validate the structure using the
RCSB developed online Validation Suite. Users will learn how to analyze the
validation report as well as PROCHECK, NUCHECK, and SFCheck results to
determine structure quality.
MSDsite Service Mr Adel Golovin EBI
The research service, MSDsite has been developed to give access to 3D active site
data. The three-dimensional environments of ligand binding sites have been derived
from the parsing and loading of the PDB entries into a relational database. For each
bound molecule the biological assembly of the quaternary structure has been used to
determine all contact residues and a fast interactive search and retrieval system has
been developed. Prosite pattern and short sequence search options are available
together with a novel graphical query generator for inter-residue contacts.
Dimitris Dimitropoulos
MSDchem tutorial: Chemistry as the starting point of a search. Following the path
from ligand chemistry to protein structure. This tutorial demonstrates in detail the
searching capabilities of the MSD database and the MSDchem tool in identifiying
ligands using their basic chemical topology and chemical signature.
MSDmySQL tutorial: Working with the bare MSD database in the popular mySQL
form using directly general purpose standard API's and programming languages. A
way to use the MSD database infrastructure in the most flexible and powerfull way.
MSDmine tutorial: A web application for scientific discovery, data analysis and
knowledge mining for the advanced researcher of the MSD database. From the
simplest to most complex searches, that combine many different information entities,
together with visualisation and cross-references. Online generation of charts and
data drill and roll-up operations.
1. Golovin, T. J. Oldfield, J. G. Tate, S. Velankar, G. J. Barton, H. Boutselakis, D.
Dimitropoulos, J. Fillon, A. Hussain, J. M. C. Ionides, M. John, P. A. Keller, E.
Krissinel, P. McNeil, A. Naim, R. Newman, A. Pajon, J. Pineda, A. Rachedi, J.
Copeland, A. Sitnov, S. Sobhany, A. Suarez-Uruena, G. J. Swaminathan, M. Tagari,

S. Tromm, W. Vranken and K. Henrick (2004) E-MSD: an integrated data resource for
bioinformatics. Nucleic Acids Research, 32, Database issue D211-D216
2. [4] Boutselakis, H., Dimitropoulos, D., Fillon, J., Golovin, A., Henrick, K., Hussain, A.,
Ionides, J., John, M., Keller, P.A., Krissinel, E., McNeil, P., Naim, A., Newman, R.,
Oldfield, T., Pineda, J., Rachedi, A., Copeland, J., Sitnov, A., Sobhany, S., Suarez-
Uruena, A., Swaminathan, J., Tagari, M., Tate, J., Tromm, S., Velankar, S. and
Vranken, W. (2003) E-MSD: the European Bioinformatics Institute Macromolecular
Structure Database Nucleic Acids Research, 31, 458-462

Thursday
Morning session Group 1
�� Tutorials – Using MSD Tools to solve Problems (60min)
��Tutorials Database Replication and use on your Desktop (60min)
MSDmySQL and MSDmine
��Tutorials –Scripting languages + data mining Dr Jaime Prilusky
Weizmann Israel (90min)
Morning session Group 2
Lectures and Demos
��Visualisation data mining Dr Thomas Oldfield EBI
��What is visualisation and how are complex data Represented?
Professor Bob Spence (Imperial College London)
��Small Structural and Sequence Motifs Dr James Milner-White
University of Glasgow
Afternoon session Group 1
Lectures and Demos
��Visualisation data mining Dr Thomas Oldfield EBI
��What is visualisation and how are complex data Represented?
Professor Bob Spence (Imperial College London)
��Small Structural and Sequence Motifs Dr James Milner-White
University of Glasgow
Afternoon session Group 2
�� Tutorials – Using MSD Tools to solve Problems (60min)
��Tutorials Database Replication and use on your Desktop (60min)
MSDmySQL & MSDmine
��Tutorials –Scripting languages + data mining Dr Jaime Prilusky
Weizmann Israel (90min)
Scripting languages + data mining Dr Jaime Prilusky Weizmann Israel
Use of fast scripting languages (Perl, Python) for creating ad-hoc searching and
analysis tools on top of existing databases
Visualisation
1. Tate,J.G., Moreland,J.L. and Bourne,P.E. (2001) Design and
implementation of a collaborative molecular graphics environment. J.
Mol. Graph. Model., 19, 280-287, 369-273.
2. Neshich,G., Togawa,R.C., Mancini,A.L., Kuser,P.R., Yamagishi,M.E.,
Pappas,G.,Jr, Torres,W.V., Fonseca e Campos,T., Ferreira,L.L.,
Luna,F.M. et al. (2003) STING Millennium: a web-based suite of
programs for comprehensive and simultaneous analysis of protein
structure and sequence. Nucleic Acids Res., 31, 3386-3392.
3. Bob Spence The Acquisition of Insight
http://www.ee.ic.ac.uk/research/information/www/Bobs.html

4. Watson, J. D. and Milner-White, E. J. 2002 The conformations of Polypeptide
Chains where the main-chain parts of successive residues are enantiomeric.
Their occurrence in Cation and Anion-binding regions of proteins. Journal of
Molecular Biology 315, 183-191
5. Watson, J. D. and Milner-White, E. J. 2002 A novel main-chain anion-binding
site in proteins: The Nest. A particular combination of phi,psi values in
successive residues gives rise to anion-binding sites that occur commonly
and are found often at functionally important regions. Journal of Molecular
Biology 315, 171-182
6. Wan, W. Y. and Milner-White, E. J. 1999 A natural grouping of motifs with an
aspartate or asparagine residue forming two hydrogen bonds to residues
ahead in sequence: Their occurrence at alpha-helical N termini and in other
situations. Journal of Molecular Biology 286, 1633-1649
7. Furnas, G.W. Generalised fisheye views. (1986) Human Factors in
Computing Systems. CHI’86 Conference Proceeedings, Boston, April 13-17
pp 16-23.
8. George G. Robertson, Jock D. Mackinlay and Stuart K. Card. (1991) The
Perspective wall: Detail and context smoothly integrated CHI’91 Conferenve
Proceedings, pp 174-179.
9. Oldfield, T.J. Creating structure features by datamining the PDB to use as
molecular replacement models. Acta Cryst D57 1421-1427.

Friday 24 th September
9:00-9-40 Summary of 3D data and WWW Current scientific resources
for 3D structure (Dr Kim Henrick EBI)
��Clean data
��Data base architectures
9:40-10:20 Now you have seen the services – a word about Conceptual
basis for data analysis, problem solving and critical thinking – (Dr Tom
Oldfield EBI)
10:20-11:00 Data Interchange/API’s Data Integration problems / Query
Interchange – Dr John Westbrook (RCSB)
11:00-11:30 coffee break
11:30-12:10 FeedBack – Wrapup – Chair Dr Helen Berman (RCSB)
12:10 Lunch and Depart